Onboard Mission Replanning for Adaptive Cooperative Multi-Robot Systems

TL;DR

This paper introduces a novel on-board replanning algorithm for cooperative multi-robot systems using Graph Attention Networks, enabling rapid, resilient mission adaptation in dynamic environments with significant efficiency gains.

Contribution

It formulates a new cooperative mission replanning problem as a variant of multiple TSP and develops a graph-based neural model for effective, fast on-board solutions.

Findings

Achieves 90% performance within 10% of LKH3 heuristic.

Runs 85-370 times faster on Raspberry Pi.

Demonstrates effective cooperation in drone missions.

Abstract

Cooperative autonomous robotic systems have significant potential for executing complex multi-task missions across space, air, ground, and maritime domains. But they commonly operate in remote, dynamic and hazardous environments, requiring rapid in-mission adaptation without reliance on fragile or slow communication links to centralised compute. Fast, on-board replanning algorithms are therefore needed to enhance resilience. Reinforcement Learning shows strong promise for efficiently solving mission planning tasks when formulated as Travelling Salesperson Problems (TSPs), but existing methods: 1) are unsuitable for replanning, where agents do not start at a single location; 2) do not allow cooperation between agents; 3) are unable to model tasks with variable durations; or 4) lack practical considerations for on-board deployment. Here we define the Cooperative Mission Replanning Problem as a novel variant of multiple TSP with adaptations to overcome these issues, and develop a new encoder/decoder-based model using Graph Attention Networks and Attention Models to solve it effectively and efficiently. Using a simple example of cooperative drones, we show our replanner consistently (90% of the time) maintains performance within 10% of the state-of-the-art LKH3 heuristic solver, whilst running 85-370 times faster on a Raspberry Pi. This work paves the way for increased resilience in autonomous multi-agent systems.